Structural information of phylogenetic tree topologies plays an important role in phylogenetic inference. However, finding appropriate topological structures for specific phylogenetic inference tasks often requires significant design effort and domain expertise. In this paper, we propose a novel structural representation method for phylogenetic inference based on learnable topological features. By combining the raw node features that minimize the Dirichlet energy with modern graph representation learning techniques, our learnable topological features can provide efficient structural information of phylogenetic trees that automatically adapts to different downstream tasks without requiring domain expertise. We demonstrate the effectiveness and efficiency of our method on a simulated data tree probability estimation task and a benchmark of challenging real data variational Bayesian phylogenetic inference problems.
翻译:植物遗传树表层结构信息在植物遗传推导中起着重要作用,然而,为具体的植物遗传推导任务找到适当的地形结构往往需要大量的设计努力和领域专门知识。在本文件中,我们提议了一种基于可学习的地形特征的植物遗传推理结构代表方法。通过将尽量减少二氧化能源的原始节点特征与现代图表表述教学技术相结合,我们可学习的地形特征可以提供高效的植物遗传树结构信息,这些树可以自动适应不同的下游任务,而不需要域内的专门知识。我们展示了模拟数据树概率估计任务的方法的有效性和效率,以及一个挑战实际数据变异性贝氏植物遗传基因问题的基准。